Relational Database Systems Research Articles

A System for the Analysis of Historical Electoral Results of the National Electoral Institute of Mexico

This paper presents a computer system capable of analyzing the databases of the results of the elections in Mexico, using genetic algorithms to achieve efficient analyses that will serve for the creation of reports with the information generated, in a shorter time than it can take to perform a manual analysis. The developed system implements the Windows Forms environment with the C# programming language and the SQL Server relational database system. In this research, the variables related to the execution time in the generation of reports will be measured, with respect to the traditional method, in addition to collecting relevant information that the system shows through experimentation.

Artificial Intelligence Based Essay Grading System

There are quite a number of challenges faced by teaching staff which if ameliorated can help reduce the quality of time spent on monotonous task associated with students essays grading More so, these days in Nigeria and other related parts of the world institutions deal with a large number of students. These however make grading time consuming and costly, therefore an automated system that can handle the tasks is highly desirable. The developed automated essay grading system in this research is restricted to grading of short and structured essay responses. Java programming language was used for the implementation and MySQL relational database system with WAMPSERVER as the back end. The results showed the user-friendly modules which enable users to navigate through various interfaces easily and work as required. The developed system has an Exact Agreement rate of 0.4 and a Pearson Correlation of 0.93 with human graders.

SplitDF: Splitting Dataframes for Memory-Efficient Data Analysis

Dataframe is a popular construct in data analysis libraries that offers a tabular view of the data. However, data within a dataframe often has redundancy, which can lead to high memory utilization of data analysis libraries. Inspired by the process of normalization in relational database systems, we propose a technique called splitting that can be applied to tabular data to reduce redundancy. Splitting involves performing lossless join decomposition by explicitly adding joining keys, and unlike normalization, splitting can be applied to tabular data without the need to perform functional dependency discovery. A split dataframe provides the same unified tabular view to the data, while internally operating on split data to improve memory efficiency. We develop SplitDF, an implementation of split dataframes in Ibis for DuckDB backend, which enables data analysis on split data with minimal changes to the Ibis API. Generation of split tabular data is automated using an algorithm SplitGen implemented in Velox. In our analysis involving ten handwritten notebooks running on SplitDF, we observe a reduction in memory usage of 19--61% when operating on split data as compared to operating on original data.

Tinjauan Atas Prosedur Flagging Peserta Taspen Dalam Rangka Pengajuan Kredit

Flagging is the giving of a sign or binding to a customer so that the customer is not easily moved or taken over by a bank or other financial institution as long as the customer still has loans at the related bank. The process of marking or binding is carried out on the related bank database system for verification by the first party.The purpose of this research is to find out the Flagging Procedures for Taspen Participants in the Context of Applying for Credit at PT. West Java and Banten Regional Development Bank, Tbk. KCP Cicurug. In order to achieve clear and directed research objectives, the authors formulate the problems that will be discussed regarding the flagging procedure for Taspen participants in the context of applying for credit, the flagging procedure for Taspen participants in the context of applying for credit is in accordance with the SOP of Bank BJB KCP Cicurug.Based on the results of the research and discussion, the flagging procedure for Taspen participants in the context of applying for credit at Bank BJB KCP Cicurug is carried out simultaneously with the credit agreement. Debtors who are participants in this taspen only need to fill out an approval form or a flagging form when the debtor enters into a credit agreement. Flagging carried out by Bank BJB KCP Cicurug is carried out for Civil Servant debtors who have a fixed salary and as taspen participants who will later be bound by Bank BJB KCP Cicurug so that these debtors cannot move to other banks or institutions as long as they still have loans at Bank BJB KCP Cicurug. Flagging is carried out by Bank BJB KCP Cicurug officers through the flagging.taspen.co.id website. Then when Bank BJB KCP Cicurug officers flagged a debtor, the data filled in must be in accordance with the form filled out by the previous debtor so that the flagging carried out for the debtor is approved by the first party.

Performance Analysis and Improvement for CRUD Operations in Relational Databases from Java Programs Using JPA, Hibernate, Spring Data JPA

The role of databases is to allow for the persistence of data, no matter if they are of the SQL or NoSQL type. In SQL databases, data are structured in a set of tables in the relational database model, grouped in rows and columns. CRUD operations (create, read, update, and delete) are used to manage the information contained in relational databases. Several dialects of the SQL language exist, as well as frameworks for mapping Java classes (models) to a relational database. The question is what we should choose for our Java application, and why? A comparison of the most frequently used relational database management systems, mixed with the most frequently used frameworks should give us some guidance about when to use what. The evaluation is conducted based on the time taken for each CRUD operation to run, from thousands to hundreds of thousands of entries, using the possible combinations in the relational database system and the framework. Aiming to assess and improve the performance, the experiments included the possibility of warming-up the Java Virtual Machine before the execution of queries. Also, the research investigated the time spent using different methods of code to determine the critical regions (bottlenecks). Thus, the conclusions provide a comprehensive overview of the performances of Java applications accessing databases depending on the suite decisions considering the database type, the framework in use, and the type of operation, with clear comparisons between the alternatives, the key findings of the advantages and drawbacks of each of them, and supporting architects and developers in their technological decisions and improving the speed of their programs.

A Survey on Relational Database Based Multi Relational Classification Algorithms

Classification on real world database is an important task in data mining. Many classification algorithms can build model only for data in single flat file as input, whereas most of real-world data bases are stored in multiple tables and managed by relational database systems. As conversion of relational data from multiple tables into a single flat file usually causes many problems, development of multi relational classification algorithms becomes popular area of research interests. Relational database based multi relational classification algorithms aim to build a model that can predict class label of unknown tuple with the help of background table knowledge. This method keeps database in it normalized form without distorting structure of database. This paper presents survey of existing multi relational classification algorithms based on relational database.

Some new fuzzy query processing methods based on similarity measurement and fuzzy data clustering

In relational and object-oriented database systems there is always data that is naturally fuzzy or uncertain. However, to deal with complex data types with fuzzy nature, these systems have many limitations. Therefore, in order to represent and manage fuzzy data, it is necessary to have a fuzzy interrogation system to facilitate non-expert users. To solve this challenge, the paper proposes two different approaches to increase the flexibility of the fuzzy interrogation system. Firstly, based on similarity measures and fuzzy logic, we develop three fuzzy query processing algorithms for single-condition and multi-condition cases such as FQSIMSC (Fuzzy Query Sim Single Condition), FQSIMMC (Fuzzy Query Sim Multi-Condition) and FQSEM (Fuzzy Query SEM). Secondly, we combine the fuzzy clustering algorithm EMC (Expectation maximization Coefficient) and the query processing algorithm that is based on fuzzy partitions FQINTERVAL (Fuzzy Query Interval). With this approach, we not only improve query processing cost but also support applications and devices equipped with intelligent interactive function that easily interacts with the fuzzy query system. The results of our theoretical and experimental analysis, it can be seen that both the proposed methods significantly reduce the processing time and memory space for a data set (extracted from UCI) that has a fuzzy and incomplete natural element with the resulting data size being optimal

A Comparative Analysis of Software Aging in Relational Database System Environments

A Comparative Analysis of Software Aging in Relational Database System Environments

AI-driven molecular generation of not-patented pharmaceutical compounds using world open patent data

Developing compounds with novel structures is important for the production of new drugs. From an intellectual perspective, confirming the patent status of newly developed compounds is essential, particularly for pharmaceutical companies. The generation of a large number of compounds has been made possible because of the recent advances in artificial intelligence (AI). However, confirming the patent status of these generated molecules has been a challenge because there are no free and easy-to-use tools that can be used to determine the novelty of the generated compounds in terms of patents in a timely manner; additionally, there are no appropriate reference databases for pharmaceutical patents in the world. In this study, two public databases, SureChEMBL and Google Patents Public Datasets, were used to create a reference database of drug-related patented compounds using international patent classification. An exact structure search system was constructed using InChIKey and a relational database system to rapidly search for compounds in the reference database. Because drug-related patented compounds are a good source for generative AI to learn useful chemical structures, they were used as the training data. Furthermore, molecule generation was successfully directed by increasing and decreasing the number of generated patented compounds through incorporation of patent status (i.e., patented or not) into learning. The use of patent status enabled generation of novel molecules with high drug-likeness. The generation using generative AI with patent information would help efficiently propose novel compounds in terms of pharmaceutical patents. Scientific contribution: In this study, a new molecule-generation method that takes into account the patent status of molecules, which has rarely been considered but is an important feature in drug discovery, was developed. The method enables the generation of novel molecules based on pharmaceutical patents with high drug-likeness and will help in the efficient development of effective drug compounds.

Abstract 14379: Get With The Guidelines ® Registry Data Tools and Analysis Platform

Introduction: The American Heart Association (AHA) Get With The Guidelines ® (GWTG) registry has emerged as a vital resource in hospital-based quality improvement and advancing the standards and practices for inpatient cardiovascular/stroke care. The GWTG registry data, with over 13 million records collected from over 2600 hospitals, has resulted in over 600 peer-reviewed publications. Goal: The goals of this project were to test new technology tools, including an interactive web-based interface connected to a database in a cloud-based workspace on the AHA Precision Medicine Platform (PMP) , to simplify GWTG data queries and analysis for researchers. Methods: A new GWTG database was built using the PostgreSQL relational database system. Integration between the database and the user interface was built using the Django web framework. Data processing and quality control was done through a series of steps including schema verification, documentation generation, and de-identification. The user interface was tested within an AHA PMP workspace that provides security, privacy, statistical analysis tools and computational power. Results: The interactive user interface and GWTG database will be accessible to researchers with approved manuscript proposals conducting their own analysis on the AHA PMP. As shown in the figure, the user-friendly interface allows researchers to efficiently filter large volumes of data using structured querying language or built-in filtering functions. Researchers can create the cohort needed and perform the approved analysis, leveraging the statistical tools within the secure and private workspace on the AHA PMP. Conclusion: The AHA is committed to the FAIR (Findable, Accessible, Interoperable, Reusable) guiding principles of data. We have launched this new technology to provide streamlined access to GWTG data, powerful analysis tools, and computational power for all researchers.

The comparative performance analysis of selected relational database systems

The objective of this study was to carry out a performance analysis of the following database systems: MySQL, PostgreSQL and Microsoft SQL Server. For this purpose scripts were used to measure execution times of selecting, updating and inserting data. Furthermore, three data sets were utilized consisting of 100, 1000 and 10000 rows. The experiment included nine cases depending on the query type and the data set. For each case, thirty five test trials were conducted while first five trials were ignored i.a. because of cache storage. The statistical test was performed for the results and the trials in which the DBMS achieved best times were counted. For each case best systems were acknowledged and the most efficient system of the experiment was determined along with systems for each operation type.

Mapping land use diversity in new GRA, Idah local government area, Kogi, State: Towards sustainable township development

In light of swift urbanization and the lack of precise land use maps in urban regions, comprehending land use patterns becomes vital for efficient planning and promoting sustainable development. The objective of this study is to assess the land use pattern in order to catalyze sustainable township development in the study area. The procedure adopted involved acquiring the cadastral layout plan of the study area, scanning, and digitizing it. Additionally, satellite imagery of the area was obtained, and both the cadastral plan and satellite imagery were geo-referenced and digitized using ArcGIS 9.2 software. These processes resulted in reasonable accuracy, with a root mean square (RMS) error of 0.002 inches, surpassing the standard of 0.004 inches. The digitized cadastral plan and satellite imagery were overlaid to produce a layered digital map of the area. A social survey of the area was conducted to identify the specific use of individual plots. Furthermore, a relational database system was created in ArcCatalog to facilitate data management and querying. The research findings demonstrated the approach's effectiveness in enabling queries for the use of any particular plot, making it adaptable to a wide range of inquiries. Notably, the study revealed the diverse purposes for which different plots were utilized, including residential, commercial, educational, and lodging. An essential aspect of land use mapping is identifying areas prone to risks and hazards, such as rising sea levels, flooding, drought, and fire. The research contributes to sustainable township development by pinpointing these vulnerable zones and providing valuable insights for urban planning and risk mitigation strategies. This is a valuable resource for urban planners, policymakers, and stakeholders, enabling them to make informed decisions to optimize land use and promote sustainable development in the study area.

Sequential Data Processing for IMERG Satellite Rainfall Comparison and Improvement Using LSTM and ADAM Optimizer

This study introduces a systematic methodology whereby different technologies were utilized to download, pre-process, and interactively compare the rainfall datasets from the Integrated Multi-Satellite Retrievals for Global Precipitation Mission (IMERG) satellite and rain gauges. To efficiently handle the large volume of data, we developed automated shell scripts for downloading IMERG data and storing it, along with rain gauge data, in a relational database system. Hypertext pre-processor (pHp) programs were built to visualize the result for better analysis. In this study, the performance of IMERG estimations over the east coast of Peninsular Malaysia for the duration of 10 years (2011–2020) against rain gauge observation data is evaluated. Moreover, this study aimed to improve the daily IMERG estimations with long short-term memory (LSTM) developed with Python. Findings show that the LSTM with Adaptive Moment Estimation (ADAM) optimizer trained against the mean square error (MSE) loss enhances the accuracy of satellite estimations. At the point-to-pixel scale, the correlation between satellite estimations and ground observations was increased by about 15%. The bias was reduced by 81–118%, MAE was reduced by 18–59%, the root-mean-square error (RMSE) was reduced by 1–66%, and the Kling–Gupta efficiency (KGE) was increased by approximately 200%. The approach developed in this study establishes a comprehensive and scalable data processing and analysis pipeline that can be applied to diverse datasets and regions encountering similar domain-specific challenges.

Technical Perspective: Conjunctive Queries with Comparisons

Query processing, the art of efficiently executing a relational query on a given database, is a foundational and core area in data management research. Established at the dawn of relational database systems in the 1970's, relational query processing remains a highly relevant and vibrant research topic today as recent work shows that, apart from its application in traditional database scenarios, it is also highly effective in optimizing machine learning workloads [1].

Machine-Learning Approach for Predicting Harmful Diseases using Big Data and IoT: A Review

Abstract: The magnitude of the enormous quantity of data in the healthcare sector is currently on the order of petabytes, and it is expanding exponentially. The massive increase of data has resulted in a number of issues with computing, storage, and transfer. With the aid of a conventional relational database system, this type of data may be evaluated and processed. Moreover, structured data can only be processed by standard database systems. Big data, on the other hand, stores unstructured data. ICT would be able to assist society in a better storage with invention of new and effective mechanisms for the storage and accessing of the information. ICT implementation in the healthcare sector is referred to as eHealth. The processing of data, subsequent analysis, and enhancement of the decision-making processes would all benefit from application in the health care sector in order to gather better treatment options for diseases' symptoms.

RLS Side Channels: Investigating Leakage of Row-Level Security Protected Data Through Query Execution Time

Many modern use cases of relational databases involve multi-tenancy. To allow a tenant to only access its data, relational database systems (RDBMSs) introduced row-level security (RLS). RLS enables specifying per-row access controls, which the database enforces by rewriting tenant queries to add an RLS policy filter that filters out rows the tenant is not allowed to view. Unfortunately, while RLS blocks queries from returning unauthorized data, side-effects of query execution can form a side-channel that leaks information about such secret data. This paper investigates how RLS query execution time can leak information about rows that the querying tenant is restricted from viewing. We show that in PostgreSQL and SQL Server, an attacker can craft index-using queries to learn whether a value they are not authorized to view exists in an RLS-protected table, and in some cases, how many times such a value exists in the table. Our attack succeeds in a realistic cloud setting: we successfully attack managed PostgreSQL and SQL Server database instances on AWS from virtual machines in the same and different data centers. To block the RLS time side-channel, we design a data-oblivious query scheme for the case of unique keys. We also analyze the trade-offs created by the data-oblivious approach for non-unique keys. To facilitate the evaluation of RLS attacks and defenses, we introduce a benchmark that supports multi-tenancy and RLS, which are not supported by established benchmarks such as YCSB. We implement our solution in PostgreSQL and show that it achieves security with minimal performance impact.

An integral approach for complete migration from a relational database to MongoDB

Today, computing has become an obligation in the lives of individuals and institutions alike. This magical sector uses and develops very rich, important, and sensitive tools and solutions, which make everyone's life easier. Computers with their tools have become indispensable and they have transformed the world into a small town. These tools revolve around data, the type, nature, and size of which continually increases up to zeta bytes of storage capacity, thus creating BigData. Considering that Relational databases present difficulties in the management of BigData, and Knowing that companies want to keep their data accumulated over decades of exercises and studies on the market, and also taking into account the cost impact if they kept both systems in terms of software, technical support and user training; therefore it is legitimate to find a reliable way to migrate their data from the relational system to a NoSQL system, which designed specifically to handle BigData. In this optic, several studies and approaches have been developed, but they present a lack or weaknesses in the treatment of the main components of the database, which we are going to deal with in our new and integrated approach to migration from the relational database system to MongoDB as a NoSQL system. In this article, we will present our contribution by developing a complete concept of our approach, starting with an introduction, which will be followed by a discussion of what other researchers have done in this direction, then we move on to a phase of analysis and modeling to develop the models and meta-models of the two systems: source and destination, also during this section we also present our analysis and modeling methodology, to present our global approach which divides its treatment into three axes, each of which processes a part of the RDBMS with a particular nature of data: data stored in tables, data carried on the structure of the RDBMS and data coming from the semantics of relational databases. The overall architecture of our approach, which we named ''TMSDRDND'', is formed by two layers: ''TSRSNLayer'' which deals with the transformation of the structure and the transfer of semantic data, and the ''MDRSNLayer'' layer, Which takes care of data migration using an ETL to be developed according to a specific concept and architecture and exploiting the results of the first layer.

Requirement for object-oriented database management systems in forensic science

The complex requirements of a database system for recording regulated chemicals exceed the capabilities of a relational database system. Inheritance, which is part of object-oriented programming, must also be logically transferred to chemical objects. This issue is illustrated here by means of examples of the Chemical Weapons Convention (CWC) and the German version of the New Psychoactive Substances Law (NpSG).

Secure Multi-party θ-join Algorithms Toward Data Federation

PDF HTML XML Export Cite reminder Secure Multi-party θ-join Algorithms Toward Data Federation DOI: 10.21655/ijsi.1673-7288.00298 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:Recently, many countries and regions have enacted data security policies, such as the General Data Protection Regulation proposed by the EU. The release of related laws and regulations has aggravated the problem of data silos, which makes it difficult to share data among various data owners. Data federation is a possible solution to this problem. Data federation refers to the calculation of query tasks jointly performed by multiple data owners without original data leaks using privacy computing technologies such as secure multi-party computing. This concept has become a research trend in recent years, and a series of representative systems have been proposed, such as SMCQL and Conclave. However, for the core join queries in the relational database system, the existing data federation system still has the following problems. First of all, the join query type is single, which is difficult to meet the query requirements under complex join conditions. Secondly, the algorithm performance has huge improvement space because the existing systems often call the security tool library directly, which means the runtime and communication overhead is high. Therefore, this paper proposes a join algorithm under data federation to address the above issues. The main contributions of this paper are as follows: firstly, multi-party-oriented federation security operators are designed and implemented, which can support many operations. Secondly, a federated θ-join algorithm and an optimization strategy are proposed to significantly reduce the security computation cost. Finally, the performance of the algorithm proposed in this paper is verified by the benchmark dataset TPC-H. The experimental results show that the proposed algorithm can reduce the runtime and communication overhead by 61.33% and 95.26%, respectively, compared with the existing data federation systems SMCQL and Conclave. Reference Related Cited by

Blockchain and API Development to Improve Relational Database Integrity and System Interoperability

Technological developments have resulted in a trend of cryptocurrencies that use a technology called blockchain to create and record all transactions made into a digital ledger. Along with the emergence of the trend of cryptocurrencies, this development has also resulted in crimes that hit the digital world, such as data leakage and cyber espionage. This threat can be prevented by applying blockchain technology to the database that has been used. Therefore, we need a system that can facilitate the use of blockchain technology and can process data from an existing relational database into a blockchain-based database. The system developed in this study was built with the FastAPI framework that uses the Python programming language and the React framework that uses the Javascript programming language. This system was tested using Katalon and Wireshark software to perform throughput testing and man-in-the-middle attacks. Evaluation of this system is assessed based on the average throughput time and also the results of Wireshark packet capture. The system designed in this research is expected to help overcome interoperability problems when using blockchain and improve relational database integrity. The results of the test show that the system is safe from man-in-the-middle attacks while sending data through API and has a faster throughput time than BigchainDB system by 4.151 seconds.